🤖 AI Summary
Existing optimization methods for autonomous systems are often task-specific and lack a unified protocol to support multi-round, traceable, and stable optimization processes. This work proposes EPOCH—the first general-purpose multi-round optimization protocol designed for heterogeneous environments. EPOCH enables coordinated refinement of multiple components, including prompts, model configurations, code, and rules, through a two-stage process comprising baseline construction and iterative self-improvement. It integrates role-constrained planning, implementation, and evaluation workflows, standardized command interfaces, and round-level tracking mechanisms. Empirical validation across diverse tasks demonstrates EPOCH’s effectiveness in production-grade autonomous optimization workflows, while consistently ensuring stability, reproducibility, traceability, and comprehensive evaluation throughout the optimization process.
📝 Abstract
Autonomous agents are increasingly used to improve prompts, code, and machine learning systems through iterative execution and feedback. Yet existing approaches are usually designed as task-specific optimization loops rather than as a unified protocol for establishing baselines and managing tracked multi-round self-improvement. We introduce EPOCH, an engineering protocol for multi-round system optimization in heterogeneous environments. EPOCH organizes optimization into two phases: baseline construction and iterative self-improvement. It further structures each round through role-constrained stages that separate planning, implementation, and evaluation, and standardizes execution through canonical command interfaces and round-level tracking. This design enables coordinated optimization across prompts, model configurations, code, and rule-based components while preserving stability, reproducibility, traceability, and integrity of evaluation. Empirical studies in various tasks illustrate the practicality of EPOCH for production-oriented autonomous improvement workflows.